Abstract: In one embodiment, a method includes identifying, by an image detection tool, a tank within an image of a railway environment and identifying, by the image detection tool, a railroad track within the image of the railway environment. The method also includes determining, by the image detection tool, a distance between the tank and the railroad track and comparing, by the image detection tool, the distance between the tank and the railroad track to a predetermined threshold distance. The method further includes determining, by the image detection tool, that the tank presents a hazard to the railway environment in response to comparing the distance between the tank and the railroad track to the predetermined threshold distance.

Abstract: Navigation and simulation systems and methods for minimally invasive therapy in which the navigation system imports a planning method using patient specific preoperative images. The navigation system uses intraoperative imaging during the medical procedure to update the preoperative images and provides images of tracked surgical tools along the surgical path prepared from the preoperative images.

Abstract: A system, method, and computer program product for treatment planning are disclosed. The system includes at least one processing component, at least one memory component, a training module, a retrieval module, and a plan generator. The training module generates hash codes by hashing features from data sources with data source-specific hash functions, and generates superclass hash codes by hashing the generated hash codes with at least one superclass hash function. The retrieval module extracts features from case data, and locates features from the data sources that are similar to the extracted features. The plan generator calculates outcome probabilities for the case data based on known outcomes associated with the located features.

Abstract: An AI system may receive an image. The AI system may include a first AI model trained using labeled training images including images from prior mammograms to predict cancer and a second AI model trained using labeled training images including images from current mammograms to classify mammogram images. The second AI model may be initialized using the weights of the first AI model using transfer learning. The AI system may receive a classification output indicating a likely current breast cancer diagnosis or a likelihood of the user to develop breast cancer in the future.

Abstract: Systems and methods for selectively processing image studies with an artificial intelligence system. One system includes an electronic processor configured to select an image study awaiting review and update a workflow status of the image study to a first status indicating that the image study has been claimed for review by the artificial intelligence system. The electronic processor is also configured to apply at least one of the plurality of rules to the image study to determine whether the image study is applicable for processing by the artificial intelligence system, and, in response to determining the image study is not applicable for processing by the artificial intelligence system based on the at least one of the plurality of rules, update the workflow status associated with the image study to a second status to make the image study available for claiming by a manual reviewer or another artificial intelligence system.

Abstract: A Y-shaped support has a head section having a first free end and a second end secured to a leg section, with the leg section having a first leg extending from the second end of the head section and a second leg extending from the second end of the head section. A first connector is provided as a first interlocking stitch, with the first interlocking stitch passed through open leg spaces of the first leg and open head spaces in the head section. A second connector is provided as a second interlocking stitch, with the second interlocking stitch passed through the aligned open leg spaces of the first leg and the second leg to connect the first leg to the second leg. The second end of the head section is located outside a space where the first leg is connected to the second leg.

Abstract: An apparatus for preparing a smokeable product is provided. The apparatus includes an upper chamber configured to receive feed material, a feed material cutting mechanism comprising a size reducer configured to rotate in two directions, a motor configured to rotate the feed material cutting mechanism, a controller configured to selectively reverse direction of rotation of the feed material cutting mechanism, a power source configured to drive the motor, a switch for turning the power source off and on, and a fan in the uppermost chamber configured to move feed material within the uppermost chamber to a lowermost chamber.

Abstract: A method of treating prolapse of a vagina includes supporting the vagina by implanting a sacrocolpopexy support and locating an exterior surface of the vagina between leg portions of a vaginal cuff section of the support, and securing a head section of the support to a ligament or a sacrum while isolating the head section from contact with tissue of the vagina.

Abstract: Systems and methods for selectively processing image studies with an artificial intelligence system. One system includes an electronic processor configured to select an image study awaiting review and update a workflow status of the image study to a first status indicating that the image study has been claimed for review by the artificial intelligence system. The electronic processor is also configured to apply at least one of the plurality of rules to the image study to determine whether the image study is applicable for processing by the artificial intelligence system, and, in response to determining the image study is not applicable for processing by the artificial intelligence system based on the at least one of the plurality of rules, update the workflow status associated with the image study to a second status to make the image study available for claiming by a manual reviewer or another artificial intelligence system.

Abstract: According to one embodiment of the current design, there is provided a styling apparatus used to style a user's hair. The apparatus includes a styling arm, a layer of glass positioned on the styling arm, and a film layer deposited or printed on a bottom surface of the layer of glass and positioned between the layer of glass and the styling arm. User hair placed atop the layer of glass is styled using heat passing from the film layer through the layer of glass. The film layer may comprise an ink film layer, a resistive ink film layer, a layer of Polymer Thick Film (PTF) ink, and/or a nano ink layer.

Abstract: A hair styling device comprises a base having a rear handle portion and a forward styling portion, the forward styling portion comprising a plurality of projections, the plurality of projections comprising one or more of the following: bristles, comb teeth or spaced apart, plates. The device comprises a retainer arm opposed to the base, and pivotally connected to the base so that the retainer arm can pivot from an open position to receive hair and a closed position for retaining hair in the plurality of projections. The forward section of the retainer arm does not have projections and has sufficient open space that at least 50% of the hair being styled is visible to a user.

Abstract: A method, computer system, and a computer program product for automatic labeling to train a machine learning algorithm is provided. The present invention may include labeling a medical image with at least one finding from a corresponding medical report. The present invention may include determining a localization information from the labeled medical image. The present invention may include training the machine learning algorithm with the determined localization information. The present invention may include detecting at least one candidate in a test medical image. The present invention may include generating a discrepancy list between the at least one detected candidate in the test medical image and at least one human-reported finding in a corresponding test medical report. The present invention may include, in response to determining that the generated discrepancy list is above a threshold, retraining the trained machine learning algorithm until the generated discrepancy list is below the threshold.

Abstract: An approach for training, on a computer, one or more deep learning algorithms with a plurality of mammograms with known outcomes based, at least in part, on using a set of mammograms of each patient in the plurality of mammograms. The approach includes receiving a first set of mammograms of a first patient. The first set of mammograms includes an unevaluated mammogram of the first patient and a set of prior mammograms of the first patient. The approach includes the trained convolutional neural network extracting the set of features from each mammogram of the set of mammograms of the first patient. Furthermore, the approach includes using a second deep learning algorithm of the one or more deep learning algorithms to perform an evaluation of the unevaluated mammogram of the first patient based, at least in part, on the set of prior mammograms of the first patient.

Abstract: Mechanisms are provided for implementing a patient complexity classification (PCC) computing system. The PCC computing system receives medical image study data for a patient that comprises one or more medical image data structures and one or more corresponding medical image metadata data structures. A natural language processing engine of the PCC computing system performs natural language processing on the medical image metadata data structure to extract features indicative of at least one of patient or medical image characteristics. A complexity classifier of the PCC computing system evaluates the extracted features to determine a patient complexity indicating a complexity of a medical condition of the patient. Routing logic associated with the PCC computing system routes the one or more medical image data structures and one or more corresponding medical image metadata data structures to one or more downstream patient evaluation computing systems based on the determined patient complexity.

Abstract: A system, method, and computer program product for treatment planning are disclosed. The system includes at least one processing component, at least one memory component, a training module, a retrieval module, and a plan generator. The training module generates hash codes by hashing features from data sources with data source-specific hash functions, and generates superclass hash codes by hashing the generated hash codes with at least one superclass hash function. The retrieval module extracts features from case data, and locates features from the data sources that are similar to the extracted features. The plan generator calculates outcome probabilities for the case data based on known outcomes associated with the located features.

Abstract: A method, computer system, and a computer program product for simulated follow-up imaging is provided. The present invention may include receiving a set of longitudinal imaging exam data associated with a patient. The received set of longitudinal imaging exam data may correspond to a series of repeated examinations of the patient conducted over time. The present invention may also include generating, using a trained learning model, a synthetic medical image associated with the patient. The generated synthetic medical image may correspond to a simulated future imaging exam of the patient predicted based on at least a portion of the series of repeated examinations of the patient conducted over time.

Abstract: A method for training an artificial intelligence (AI) system for improved health screening is provided. A processor of the AI system, where the AI system may include a combined AI model comprising one or more AI models, may receive training images. The processor may utilize, one or more AI models that each analyze the training images. The one or more AI models may include respective objective functions. The processor may receive, from the one or more AI models, the respective objective functions obtained after each of the one or more AI models are separately trained. The method my further involve submitted a combined weighted objective function to train the AI system. The combined weighted objective function may be a weighted combination of the respective objective function from each of the one or more AI models.

Abstract: An AI system may receive an image. The AI system may include a first AI model trained using labeled training images including images from prior mammograms to predict cancer and a second AI model trained using labeled training images including images from current mammograms to classify mammogram images. The second AI model may be initialized using the weights of the first AI model using transfer learning. The AI system may receive a classification output indicating a likely current breast cancer diagnosis or a likelihood of the user to develop breast cancer in the future.